{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chapter 9.2 - Recurrent Neural Network ",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qb1wpsF5Z9fh",
        "colab_type": "text"
      },
      "source": [
        "### 9.2. Predicting Stock Market Prices with LSTM using TensorFlow 2.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pc6dU7QvaPfh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ca639de3-5721-4bd8-de44-61d2413a1b8d"
      },
      "source": [
        "pip install --upgrade tensorflow"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ad/ad/769c195c72ac72040635c66cd9ba7b0f4b4fc1ac67e59b99fa6988446c22/tensorflow-2.3.1-cp36-cp36m-manylinux2010_x86_64.whl (320.4MB)\n",
            "\u001b[K     |███▉                            | 37.9MB 51.2MB/s eta 0:00:06\u001b[31mERROR: Exception:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_vendor/urllib3/response.py\", line 425, in _error_catcher\n",
            "    yield\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_vendor/urllib3/response.py\", line 507, in read\n",
            "    data = self._fp.read(amt) if not fp_closed else b\"\"\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_vendor/cachecontrol/filewrapper.py\", line 62, in read\n",
            "    data = self.__fp.read(amt)\n",
            "  File \"/usr/lib/python3.6/http/client.py\", line 459, in read\n",
            "    n = self.readinto(b)\n",
            "  File \"/usr/lib/python3.6/http/client.py\", line 503, in readinto\n",
            "    n = self.fp.readinto(b)\n",
            "  File \"/usr/lib/python3.6/socket.py\", line 586, in readinto\n",
            "    return self._sock.recv_into(b)\n",
            "  File \"/usr/lib/python3.6/ssl.py\", line 1012, in recv_into\n",
            "    return self.read(nbytes, buffer)\n",
            "  File \"/usr/lib/python3.6/ssl.py\", line 874, in read\n",
            "    return self._sslobj.read(len, buffer)\n",
            "  File \"/usr/lib/python3.6/ssl.py\", line 631, in read\n",
            "    v = self._sslobj.read(len, buffer)\n",
            "ConnectionResetError: [Errno 104] Connection reset by peer\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/cli/base_command.py\", line 153, in _main\n",
            "    status = self.run(options, args)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/commands/install.py\", line 382, in run\n",
            "    resolver.resolve(requirement_set)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/legacy_resolve.py\", line 201, in resolve\n",
            "    self._resolve_one(requirement_set, req)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/legacy_resolve.py\", line 365, in _resolve_one\n",
            "    abstract_dist = self._get_abstract_dist_for(req_to_install)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/legacy_resolve.py\", line 313, in _get_abstract_dist_for\n",
            "    req, self.session, self.finder, self.require_hashes\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/operations/prepare.py\", line 194, in prepare_linked_requirement\n",
            "    progress_bar=self.progress_bar\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/download.py\", line 465, in unpack_url\n",
            "    progress_bar=progress_bar\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/download.py\", line 316, in unpack_http_url\n",
            "    progress_bar)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/download.py\", line 551, in _download_http_url\n",
            "    _download_url(resp, link, content_file, hashes, progress_bar)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/download.py\", line 253, in _download_url\n",
            "    hashes.check_against_chunks(downloaded_chunks)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/utils/hashes.py\", line 80, in check_against_chunks\n",
            "    for chunk in chunks:\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/download.py\", line 223, in written_chunks\n",
            "    for chunk in chunks:\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/utils/ui.py\", line 160, in iter\n",
            "    for x in it:\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_internal/download.py\", line 212, in resp_read\n",
            "    decode_content=False):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_vendor/urllib3/response.py\", line 564, in stream\n",
            "    data = self.read(amt=amt, decode_content=decode_content)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_vendor/urllib3/response.py\", line 529, in read\n",
            "    raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\n",
            "  File \"/usr/lib/python3.6/contextlib.py\", line 99, in __exit__\n",
            "    self.gen.throw(type, value, traceback)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pip/_vendor/urllib3/response.py\", line 443, in _error_catcher\n",
            "    raise ProtocolError(\"Connection broken: %r\" % e, e)\n",
            "pip._vendor.urllib3.exceptions.ProtocolError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iq8hIGBpB1o_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7fd4f591-f91e-43aa-a89a-5776feef114c"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pR8qYy47B4bQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bbb9571a-920e-4b4b-947e-8c003d4ddda7"
      },
      "source": [
        "# mounting google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTvvDnF7E6rs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#importing dataset\n",
        "fb_complete_data = pd.read_csv(\"/gdrive/My Drive/datasets/fb_train.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pD7_fnsvFh0f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "6cc6197c-4cb8-4644-c4e0-e4508df6251f"
      },
      "source": [
        "#printing dataset header\n",
        "fb_complete_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>78.580002</td>\n",
              "      <td>78.930000</td>\n",
              "      <td>77.699997</td>\n",
              "      <td>78.449997</td>\n",
              "      <td>78.449997</td>\n",
              "      <td>18177500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>77.980003</td>\n",
              "      <td>79.250000</td>\n",
              "      <td>76.860001</td>\n",
              "      <td>77.190002</td>\n",
              "      <td>77.190002</td>\n",
              "      <td>26452200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>77.230003</td>\n",
              "      <td>77.589996</td>\n",
              "      <td>75.360001</td>\n",
              "      <td>76.150002</td>\n",
              "      <td>76.150002</td>\n",
              "      <td>27399300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>76.760002</td>\n",
              "      <td>77.360001</td>\n",
              "      <td>75.820000</td>\n",
              "      <td>76.150002</td>\n",
              "      <td>76.150002</td>\n",
              "      <td>22045300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>76.739998</td>\n",
              "      <td>78.230003</td>\n",
              "      <td>76.080002</td>\n",
              "      <td>78.180000</td>\n",
              "      <td>78.180000</td>\n",
              "      <td>23961000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date       Open       High        Low      Close  Adj Close    Volume\n",
              "0  2015-01-02  78.580002  78.930000  77.699997  78.449997  78.449997  18177500\n",
              "1  2015-01-05  77.980003  79.250000  76.860001  77.190002  77.190002  26452200\n",
              "2  2015-01-06  77.230003  77.589996  75.360001  76.150002  76.150002  27399300\n",
              "3  2015-01-07  76.760002  77.360001  75.820000  76.150002  76.150002  22045300\n",
              "4  2015-01-08  76.739998  78.230003  76.080002  78.180000  78.180000  23961000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqB1Az97Fkkw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#filtering open column\n",
        "fb_training_processed = fb_complete_data[['Open']].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAnrnM1XFxZ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#scaling features\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "fb_training_scaled = scaler.fit_transform(fb_training_processed)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93sga1IyF2vC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "64ca09a5-81e3-4b96-e8a7-987798ef4e55"
      },
      "source": [
        "len(fb_training_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1257"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBWfetp1GKBb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#training features containd data of last 60 days\n",
        "#training labels contain data of 61st day\n",
        "\n",
        "fb_training_features= []\n",
        "fb_training_labels = []\n",
        "for i in range(60, len(fb_training_scaled)):\n",
        "    fb_training_features.append(fb_training_scaled[i-60:i, 0])\n",
        "    fb_training_labels.append(fb_training_scaled[i, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmlXLCJDGe3k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#converting training data to numpy arrays\n",
        "X_train = np.array(fb_training_features)\n",
        "y_train = np.array(fb_training_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihzFf_TuNbtR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "5322ddc6-18bf-4d56-9646-ceb824d063e2"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1197, 60)\n",
            "(1197,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwVOyiqUG8uh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#converting data into 3D shape\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tS20_rION7BY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.layers import Input, Activation, Dense, Flatten, Dropout,  Flatten, LSTM\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbJjA8F2ZGej",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "a0332144-e008-4c36-f2b9-ae8ae03ee885"
      },
      "source": [
        "#defining the LSTM network\n",
        "\n",
        "input_layer = Input(shape = (X_train.shape[1], 1))\n",
        "lstm1 = LSTM(100, activation='relu', return_sequences=True)(input_layer)\n",
        "do1 = Dropout(0.2)(lstm1)\n",
        "lstm2 = LSTM(100, activation='relu', return_sequences=True)(do1)\n",
        "do2 = Dropout(0.2)(lstm2)\n",
        "lstm3 = LSTM(100, activation='relu', return_sequences=True)(do2)\n",
        "do3 = Dropout(0.2)(lstm3)\n",
        "lstm4 = LSTM(100, activation='relu')(do3)\n",
        "do4 = Dropout(0.2)(lstm4)\n",
        "\n",
        "output_layer = Dense(1)(do4)\n",
        "model = Model(input_layer, output_layer)\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Layer lstm_4 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
            "WARNING:tensorflow:Layer lstm_5 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
            "WARNING:tensorflow:Layer lstm_6 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
            "WARNING:tensorflow:Layer lstm_7 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADybUeEKNQg8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "52d10952-7fb1-4075-9794-3d09261bc122"
      },
      "source": [
        "#plotting model architecture\n",
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, to_file='/gdrive/My Drive/datasets/model_plot1.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Q7b9CAZvb0Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "7c60a923-1bdd-48ae-c748-8fb24dc2764d"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "y_train= y_train.reshape(-1,1)\n",
        "print(y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1197, 60, 1)\n",
            "(1197,)\n",
            "(1197, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twsWEX2IOBxe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b7c53d10-cbdb-496e-bc09-f761c0ec1a1c"
      },
      "source": [
        "#training the model\n",
        "model_history = model.fit(X_train, y_train, epochs=100, verbose=1, batch_size = 32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "38/38 [==============================] - 18s 469ms/step - loss: 0.0740\n",
            "Epoch 2/100\n",
            "38/38 [==============================] - 18s 464ms/step - loss: 0.0085\n",
            "Epoch 3/100\n",
            "38/38 [==============================] - 18s 466ms/step - loss: 0.0067\n",
            "Epoch 4/100\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.0062\n",
            "Epoch 5/100\n",
            "38/38 [==============================] - 18s 473ms/step - loss: 0.0059\n",
            "Epoch 6/100\n",
            "38/38 [==============================] - 18s 482ms/step - loss: 0.0060\n",
            "Epoch 7/100\n",
            "38/38 [==============================] - 18s 471ms/step - loss: 0.0053\n",
            "Epoch 8/100\n",
            "38/38 [==============================] - 17s 458ms/step - loss: 0.0052\n",
            "Epoch 9/100\n",
            "38/38 [==============================] - 18s 461ms/step - loss: 0.0053\n",
            "Epoch 10/100\n",
            "38/38 [==============================] - 18s 467ms/step - loss: 0.0048\n",
            "Epoch 11/100\n",
            "38/38 [==============================] - 18s 474ms/step - loss: 0.0050\n",
            "Epoch 12/100\n",
            "38/38 [==============================] - 18s 472ms/step - loss: 0.0046\n",
            "Epoch 13/100\n",
            "38/38 [==============================] - 18s 466ms/step - loss: 0.0051\n",
            "Epoch 14/100\n",
            "38/38 [==============================] - 18s 467ms/step - loss: 0.0045\n",
            "Epoch 15/100\n",
            "38/38 [==============================] - 17s 454ms/step - loss: 0.0044\n",
            "Epoch 16/100\n",
            "38/38 [==============================] - 18s 461ms/step - loss: 0.0045\n",
            "Epoch 17/100\n",
            "38/38 [==============================] - 17s 459ms/step - loss: 0.0046\n",
            "Epoch 18/100\n",
            "38/38 [==============================] - 18s 461ms/step - loss: 0.0039\n",
            "Epoch 19/100\n",
            "38/38 [==============================] - 17s 457ms/step - loss: 0.0043\n",
            "Epoch 20/100\n",
            "38/38 [==============================] - 17s 459ms/step - loss: 0.0048\n",
            "Epoch 21/100\n",
            "38/38 [==============================] - 17s 458ms/step - loss: 0.0040\n",
            "Epoch 22/100\n",
            "38/38 [==============================] - 17s 458ms/step - loss: 0.0044\n",
            "Epoch 23/100\n",
            "38/38 [==============================] - 18s 463ms/step - loss: 0.0040\n",
            "Epoch 24/100\n",
            "38/38 [==============================] - 18s 461ms/step - loss: 0.0038\n",
            "Epoch 25/100\n",
            "38/38 [==============================] - 17s 458ms/step - loss: 0.0038\n",
            "Epoch 26/100\n",
            "38/38 [==============================] - 18s 461ms/step - loss: 0.0043\n",
            "Epoch 27/100\n",
            "38/38 [==============================] - 18s 467ms/step - loss: 0.0041\n",
            "Epoch 28/100\n",
            "38/38 [==============================] - 18s 474ms/step - loss: 0.0035\n",
            "Epoch 29/100\n",
            "38/38 [==============================] - 17s 459ms/step - loss: 0.0040\n",
            "Epoch 30/100\n",
            "38/38 [==============================] - 17s 458ms/step - loss: 0.0041\n",
            "Epoch 31/100\n",
            "38/38 [==============================] - 17s 459ms/step - loss: 0.0035\n",
            "Epoch 32/100\n",
            "38/38 [==============================] - 18s 461ms/step - loss: 0.0034\n",
            "Epoch 33/100\n",
            "38/38 [==============================] - 17s 457ms/step - loss: 0.0036\n",
            "Epoch 34/100\n",
            "38/38 [==============================] - 18s 461ms/step - loss: 0.0035\n",
            "Epoch 35/100\n",
            "38/38 [==============================] - 17s 456ms/step - loss: 0.0038\n",
            "Epoch 36/100\n",
            "38/38 [==============================] - 17s 453ms/step - loss: 0.0035\n",
            "Epoch 37/100\n",
            "38/38 [==============================] - 17s 458ms/step - loss: 0.0037\n",
            "Epoch 38/100\n",
            "38/38 [==============================] - 18s 462ms/step - loss: 0.0035\n",
            "Epoch 39/100\n",
            "38/38 [==============================] - 17s 455ms/step - loss: 0.0033\n",
            "Epoch 40/100\n",
            "38/38 [==============================] - 17s 456ms/step - loss: 0.0036\n",
            "Epoch 41/100\n",
            "38/38 [==============================] - 17s 458ms/step - loss: 0.0035\n",
            "Epoch 42/100\n",
            "38/38 [==============================] - 17s 455ms/step - loss: 0.0034\n",
            "Epoch 43/100\n",
            "38/38 [==============================] - 17s 460ms/step - loss: 0.0034\n",
            "Epoch 44/100\n",
            "38/38 [==============================] - 18s 465ms/step - loss: 0.0034\n",
            "Epoch 45/100\n",
            "38/38 [==============================] - 18s 465ms/step - loss: 0.0032\n",
            "Epoch 46/100\n",
            "38/38 [==============================] - 17s 459ms/step - loss: 0.0032\n",
            "Epoch 47/100\n",
            "38/38 [==============================] - 17s 449ms/step - loss: 0.0035\n",
            "Epoch 48/100\n",
            "38/38 [==============================] - 17s 459ms/step - loss: 0.0029\n",
            "Epoch 49/100\n",
            "38/38 [==============================] - 17s 452ms/step - loss: 0.0035\n",
            "Epoch 50/100\n",
            "38/38 [==============================] - 17s 460ms/step - loss: 0.0029\n",
            "Epoch 51/100\n",
            "38/38 [==============================] - 18s 466ms/step - loss: 0.0027\n",
            "Epoch 52/100\n",
            "38/38 [==============================] - 17s 456ms/step - loss: 0.0029\n",
            "Epoch 53/100\n",
            "38/38 [==============================] - 18s 465ms/step - loss: 0.0029\n",
            "Epoch 54/100\n",
            "38/38 [==============================] - 17s 453ms/step - loss: 0.0029\n",
            "Epoch 55/100\n",
            "38/38 [==============================] - 18s 466ms/step - loss: 0.0029\n",
            "Epoch 56/100\n",
            "38/38 [==============================] - 18s 462ms/step - loss: 0.0028\n",
            "Epoch 57/100\n",
            "38/38 [==============================] - 18s 468ms/step - loss: 0.0026\n",
            "Epoch 58/100\n",
            "38/38 [==============================] - 18s 477ms/step - loss: 0.0027\n",
            "Epoch 59/100\n",
            "38/38 [==============================] - 18s 469ms/step - loss: 0.0026\n",
            "Epoch 60/100\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.0027\n",
            "Epoch 61/100\n",
            "38/38 [==============================] - 18s 478ms/step - loss: 0.0029\n",
            "Epoch 62/100\n",
            "38/38 [==============================] - 18s 473ms/step - loss: 0.0025\n",
            "Epoch 63/100\n",
            "38/38 [==============================] - 18s 470ms/step - loss: 0.0023\n",
            "Epoch 64/100\n",
            "38/38 [==============================] - 18s 470ms/step - loss: 0.0024\n",
            "Epoch 65/100\n",
            "38/38 [==============================] - 18s 475ms/step - loss: 0.0024\n",
            "Epoch 66/100\n",
            "38/38 [==============================] - 18s 462ms/step - loss: 0.0023\n",
            "Epoch 67/100\n",
            "38/38 [==============================] - 18s 475ms/step - loss: 0.0025\n",
            "Epoch 68/100\n",
            "38/38 [==============================] - 18s 463ms/step - loss: 0.0024\n",
            "Epoch 69/100\n",
            "38/38 [==============================] - 18s 466ms/step - loss: 0.0025\n",
            "Epoch 70/100\n",
            "38/38 [==============================] - 18s 478ms/step - loss: 0.0021\n",
            "Epoch 71/100\n",
            "38/38 [==============================] - 18s 479ms/step - loss: 0.0022\n",
            "Epoch 72/100\n",
            "38/38 [==============================] - 18s 475ms/step - loss: 0.0025\n",
            "Epoch 73/100\n",
            "38/38 [==============================] - 18s 478ms/step - loss: 0.0022\n",
            "Epoch 74/100\n",
            "38/38 [==============================] - 18s 469ms/step - loss: 0.0024\n",
            "Epoch 75/100\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.0023\n",
            "Epoch 76/100\n",
            "38/38 [==============================] - 18s 474ms/step - loss: 0.0023\n",
            "Epoch 77/100\n",
            "38/38 [==============================] - 18s 480ms/step - loss: 0.0023\n",
            "Epoch 78/100\n",
            "38/38 [==============================] - 18s 477ms/step - loss: 0.0020\n",
            "Epoch 79/100\n",
            "38/38 [==============================] - 18s 481ms/step - loss: 0.0022\n",
            "Epoch 80/100\n",
            "38/38 [==============================] - 18s 484ms/step - loss: 0.0024\n",
            "Epoch 81/100\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.0021\n",
            "Epoch 82/100\n",
            "38/38 [==============================] - 19s 487ms/step - loss: 0.0020\n",
            "Epoch 83/100\n",
            "38/38 [==============================] - 18s 486ms/step - loss: 0.0021\n",
            "Epoch 84/100\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.0023\n",
            "Epoch 85/100\n",
            "38/38 [==============================] - 18s 482ms/step - loss: 0.0021\n",
            "Epoch 86/100\n",
            "38/38 [==============================] - 18s 481ms/step - loss: 0.0022\n",
            "Epoch 87/100\n",
            "38/38 [==============================] - 18s 475ms/step - loss: 0.0021\n",
            "Epoch 88/100\n",
            "38/38 [==============================] - 18s 478ms/step - loss: 0.0022\n",
            "Epoch 89/100\n",
            "38/38 [==============================] - 18s 480ms/step - loss: 0.0020\n",
            "Epoch 90/100\n",
            "38/38 [==============================] - 18s 475ms/step - loss: 0.0019\n",
            "Epoch 91/100\n",
            "38/38 [==============================] - 18s 466ms/step - loss: 0.0018\n",
            "Epoch 92/100\n",
            "38/38 [==============================] - 18s 476ms/step - loss: 0.0021\n",
            "Epoch 93/100\n",
            "38/38 [==============================] - 18s 472ms/step - loss: 0.0023\n",
            "Epoch 94/100\n",
            "38/38 [==============================] - 18s 466ms/step - loss: 0.0020\n",
            "Epoch 95/100\n",
            "38/38 [==============================] - 18s 468ms/step - loss: 0.0020\n",
            "Epoch 96/100\n",
            "38/38 [==============================] - 18s 473ms/step - loss: 0.0018\n",
            "Epoch 97/100\n",
            "38/38 [==============================] - 18s 467ms/step - loss: 0.0019\n",
            "Epoch 98/100\n",
            "38/38 [==============================] - 17s 459ms/step - loss: 0.0019\n",
            "Epoch 99/100\n",
            "38/38 [==============================] - 18s 468ms/step - loss: 0.0017\n",
            "Epoch 100/100\n",
            "38/38 [==============================] - 18s 465ms/step - loss: 0.0018\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDZkZX3Cvada",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtyfvdJ6OOC4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating test set\n",
        "fb_testing_complete_data = pd.read_csv(\"/gdrive/My Drive/datasets/fb_test.csv\")\n",
        "fb_testing_processed = fb_testing_complete_data[['Open']].values\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrKzx5LKlwdL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fb_all_data = pd.concat((fb_complete_data['Open'], fb_testing_complete_data['Open']), axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKYjCvZtau3M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2fa866d0-b006-44b6-82eb-f0b6dd95cc2f"
      },
      "source": [
        "test_inputs = fb_all_data [len(fb_all_data ) - len(fb_testing_complete_data) - 60:].values\n",
        "print(test_inputs.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(80,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHZ_--5NotN9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "509e2d72-277c-4f24-9583-e29a3a192ee2"
      },
      "source": [
        "test_inputs = test_inputs.reshape(-1,1)\n",
        "test_inputs = scaler.transform(test_inputs)\n",
        "print(test_inputs.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(80, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCYxBZqMouy2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fb_test_features = []\n",
        "for i in range(60, 80):\n",
        "    fb_test_features.append(test_inputs[i-60:i, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CdxXBF-pmKd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4937d873-2077-43b3-9e47-19b124610bd4"
      },
      "source": [
        "X_test = np.array(fb_test_features)\n",
        "print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20, 60)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PgsXE8dptw4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fab8b28c-d54d-4a1c-f5f1-cbe514946ba7"
      },
      "source": [
        "#converting test data into 3D shape\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20, 60, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPbYX-TZp900",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5b9b3eb9-3abb-41c7-9626-c5d5ea7a5656"
      },
      "source": [
        "#making predicitons on test set\n",
        "y_pred =  model.predict(X_test)\n",
        "print(y_pred.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqejpICmqEeh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#converting scaled data back to original data\n",
        "y_pred = scaler.inverse_transform(y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZQBlwZtqIKC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "a67a72ea-7578-442b-cce6-9d270131dc51"
      },
      "source": [
        "#plotting original and predicted stock values\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.plot(fb_testing_processed, color='red', label='Actual Facenook Stock Price')\n",
        "plt.plot(y_pred , color='green', label='Predicted Face Stock Price')\n",
        "plt.title('Facebook Stock Prices')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxqsNVfrzMlH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}